Identification.
Out of all possible object pairs, we consider those which have at least 25 training images in Both (\(\approx {}2700\) ).
From these, SPIRE identified 29 where the model's prediction changes at least 40% of the time when we remove Spurious; we verified that the model is relying on these SPs by checking that it has large recall and hallucination gaps.
Table REF  shows a few of the identified SPs; overall, they are quite diverse:
the spurious object ranges from common (e.g., person) to rare (e.g., sheep);
the SPs range from objects that are commonly co-located (e.g., tie-person) to usually separate (e.g., dog-sheep);
and a few objects (e.g., tie and frisbee) have more than one associated SP.
Notably, SPIRE identifies negative SPs (e.g., tie-cat) while prior work [1]}, [2]}, [3]} has only found positive SPs (e.g., frisbee-person and fork-dining table).
